This video is part of the Deep Learning Summit - Track 1, Montreal, 2017 Event. If you would like to access all of the videos please click here.

Panel of Pioneers (Half version)

Joelle Pineau, Associate Professor at McGill University

Joelle Pineau is an Associate Professor and William Dawson Scholar at McGill University where she co-directs the Reasoning and Learning Lab. Dr. Pineau’s research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Artificial Intelligence Research and the Journal of Machine Learning Research and is currently President-Elect of the International Machine Learning Society. She is a Senior Fellow of the Canadian Institute for Advanced Research and in 2016 was named a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada.

Geoffrey Hinton, Professor at University of Toronto

Geoffrey Hinton designs machine learning algorithms. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification.

Yann LeCun, Director of AI Research at Facebook

Yann is the Director of AI Research at Facebook since December 2013, and Silver Professor at New York University on a part time basis, mainly affiliated with the NYU Center for Data Science, and the Courant Institute of Mathematical Science. He received the EE Diploma from Ecole Supérieure d’Ingénieurs en Electrotechnique et Electronique (ESIEE Paris), and a PhD in CS from Université Pierre et Marie Curie (Paris). After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. He is the co-director of the Neural Computation and Adaptive Perception Program of CIFAR, and co-lead of the Moore-Sloan Data Science Environments for NYU. He received the 2014 IEEE Neural Network Pioneer Award.

Yoshua Bengio, Full Professor at Université de Montréal

Yoshua Bengio (PhD in CS, McGill University, 1991), post-docs at M.I.T. (Michael Jordan) and AT&T Bell Labs (Yann LeCun), CS professor at Université de Montréal, Canada Research Chair in Statistical Learning Algorithms, NSERC Chair, CIFAR Fellow, member of NIPS foundation board and former program/general chair, co-created ICLR conference, authored two books and over 300 publications, the most cited being in the areas of deep learning, recurrent networks, probabilistic learning, natural language and manifold learning. He is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks.